Automatic Extraction of Man-Made Objects from Aerial and Space Images (II)
01 Aug 1995-
TL;DR: The role of Artificial Intelligence in the Reconstruction of Man-Made Objects from Aerial Images was highlighted by DARPA's Research Program in Automatic Population of Geospatial Databases.
Abstract: General Topics and Scene Reconstruction- An Overview of DARPA's Research Program in Automatic Population of Geospatial Databases- A Testbed for the Evaluation of Feature Extraction Techniques in a Time Constrained Environment- The Role of Artificial Intelligence in the Reconstruction of Man-Made Objects from Aerial Images- Scene Reconstruction Research - Towards an Automatic System- Semantic Modelling of Man-Made Objects by Production Nets- From Large-Scale DTM Extraction to Feature Extraction- Building Detection and Reconstruction- 3-D Building Reconstruction with ARUBA: A Qualitative and Quantitative Evaluation- A System for Building Detection from Aerial Images- On the Reconstruction of Urban House Roofs from Aerial Images- Image-Based Reconstruction of Informal Settlements- A Model Driven Approach to Extract Buildings from Multi-View Aerial Imagery- Automated Building Extraction from Digital Stereo Imagery- Application of Semi-Automatic Building Acquisition- On the Integration of Object Modeling and Image Modeling in Automated Building Extraction from Aerial Images- TOBAGO - A Topology Builder for the Automated Generation of Building Models- Crestlines Constribution to the Automatic Building Extraction- Recognizing Buildings in Aerial Image- Above-Ground Objects in Urban Scenes from Medium Scale Aerial Imagery- Digital Surface Models for Building Extraction- Extracting Artificial Surface Objects from Airborne Laser Scanner Data- Interpretation of Urban Surface Models using 2D Building Information- Least Squares Matching for Three Dimensional Building Reconstruction- Assessment of the Effects of Resolution on Automated DEM and Building Extraction- Road Extraction- The Role of Grouping for Road Extraction- Artificial Intelligence in 3-D Feature Extraction- Updating Road Maps by Contextual Reasoning- Fast Robust Tracking of Curvy Partially Occluded Roads in Clutter in Aerial Images- Linear Feature Extraction with 3-D LSB-Snakes- Context-Supported Road Extraction- Map/GIS-Based Methods- Three-Dimensional Description of Dense Urban Areas using Maps and Aerial Images- MOSES: A Structural Approach to Aerial Image Understanding- An Approach for the Extraction of Settlement Areas- Extraction of Polygonal Features from Satellite Images for Automatic Registration: The ARCHANGEL Project- Visualisation- A Set of Visualization Data Needs in Urban Environmental Planning & Design for Photogrammetric Data- A Virtual Reality Model of a Major International Airport- Managing Large 3D Urban Database Contents Supporting Phototexture and Levels of Detail- List of Workshop Participants- Author Index
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TL;DR: By analyzing the scale-space behavior of a model line profile, it is shown how the bias that is induced by asymmetrical lines can be removed and the algorithm not only returns the precise subpixel line position, but also the width of the line for each line point, also with subpixel accuracy.
Abstract: The extraction of curvilinear structures is an important low-level operation in computer vision that has many applications. Most existing operators use a simple model for the line that is to be extracted, i.e., they do not take into account the surroundings of a line. This leads to the undesired consequence that the line will be extracted in the wrong position whenever a line with different lateral contrast is extracted. In contrast, the algorithm proposed in this paper uses an explicit model for lines and their surroundings. By analyzing the scale-space behavior of a model line profile, it is shown how the bias that is induced by asymmetrical lines can be removed. Furthermore, the algorithm not only returns the precise subpixel line position, but also the width of the line for each line point, also with subpixel accuracy.
1,200 citations
TL;DR: Although airborne laser scanning competes to a certain extent with photogrammetry and will replace it in certain cases, the two technologies are fairly complementary and their integration can lead to more accurate and complete products, and open up new areas of application.
Abstract: A comparison between data acquisition and processing from passive optical sensors and airborne laser scanning is presented. A short overview and the major differences between the two technologies are outlined. Advantages and disadvantages with respect to various aspects are discussed, like sensors, platforms, flight planning, data acquisition conditions, imaging, object reflectance, automation, accuracy, flexibility and maturity, production time and costs. A more detailed comparison is presented with respect to DTM and DSM generation. Strengths of laser scanning with respect to certain applications are outlined. Although airborne laser scanning competes to a certain extent with photogrammetry and will replace it in certain cases, the two technologies are fairly complementary and their integration can lead to more accurate and complete products, and open up new areas of application.
729 citations
TL;DR: This article presents an overview of existing map processing techniques, bringing together the past and current research efforts in this interdisciplinary field, to characterize the advances that have been made, and to identify future research directions and opportunities.
Abstract: Maps depict natural and human-induced changes on earth at a fine resolution for large areas and over long periods of time. In addition, maps—especially historical maps—are often the only information source about the earth as surveyed using geodetic techniques. In order to preserve these unique documents, increasing numbers of digital map archives have been established, driven by advances in software and hardware technologies. Since the early 1980s, researchers from a variety of disciplines, including computer science and geography, have been working on computational methods for the extraction and recognition of geographic features from archived images of maps (digital map processing). The typical result from map processing is geographic information that can be used in spatial and spatiotemporal analyses in a Geographic Information System environment, which benefits numerous research fields in the spatial, social, environmental, and health sciences. However, map processing literature is spread across a broad range of disciplines in which maps are included as a special type of image. This article presents an overview of existing map processing techniques, with the goal of bringing together the past and current research efforts in this interdisciplinary field, to characterize the advances that have been made, and to identify future research directions and opportunities.
674 citations
Cites background from "Automatic Extraction of Man-Made Ob..."
...In comparison, road extraction from satellite imagery identifies and extracts features from spectral reflectance data that could cartographically be represented as road areas and road centerlines in a GIS [Hickman et al. 1995; Steger et al. 1997; Hodgson et al. 2004]....
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01 Jan 2001
TL;DR: The authors argue for classification of homogeneous groups of pixels reflecting the authors' objects of interest in reality and use algorithms to delineate objects based on contextual information in an image on the basis of texture or fractal dimension.
Abstract: While remote sensing made enormous progress over the last years in terms of improved resolution, data availability and public awareness, a vast majority of applications rely on basic image processing concepts developed in the 70s: per-pixel classification of in a multi-dimensional feature space. It is argued that this methodology does not make use of any spatial concepts. Especially in high-resolution images it is very likely that neighbouring pixels belong to the same land cover class as the pixel under consideration. The authors argue for classification of homogeneous groups of pixels reflecting our objects of interest in reality and use algorithms to delineate objects based on contextual information in an image on the basis of texture or fractal dimension. ZUSAMMENFASSUNG Was ist mit den Pixeln los? Neue Entwicklungen zur Integration von Fernerkundung und GIS. Fernerkundung hat sich in den vergangenen Jahren bezüglich Bildauflösung, Datenverfügbarkeit und öffentlicher Präsenz enorm weiterentwickelt, trotzdem basieren nahezu alle Anwendungen auf den methodischen Grundlagen der Bildverarbeitung aus den 70er Jahren: individuelle Pixel werden im mehrdimensionalen Spektralraum klassifiziert, ohne irgendwelche räumlichen Konzepte zu berücksichtigen. Insbesondere bei hochauflösenden Bildern gehören benachbarte Pixel mit hoher Wahrscheinlichkeit zur selben Kategorie wie das aktuelle Pixel. Die Autoren argumentieren für Klassifikationsansätze homogener Gruppen von Pixeln, die realweltlichen Objekten entsprechen und aus kontextueller Bildinformation (Textur, fraktale Dimension) abgeleitet werden. Dr. Thomas Blaschke 1 Patterns do matter, or: the need for change We start our considerations of recent remote sensing practice from the user’s point of view and, more precisely, from a geographical or landscape ecology point of view: The world in its complexity and manifold relationships cannot easily be grasped in full depth. Creating models of the world or computer-based representations of its surface poses a series of problems. In landscape ecology, there is a growing awareness about continuity of phenomena and discontinuities of scales. Forman (1995) described this ambiguity through the metaphor of a person gradually descending with a spaceship or balloon. Human perception abruptly starts to discover patterns and mosaics. Many mosaics are quasi-stable or persistent for a while, separated by rapid changes that represent the “domains of scale”. Each domain exhibits certain spatial patterns, which in turn are produced by a certain causal mechanism or group of processes. Back to remote sensing: The ultimate goal is to mirror, elucidate, quantify and to describe surface patterns in order to contribute to an understanding of the underlying phenomena and processes. Since the start of the first Landsat satellite in 1972, we achieve this in more or less the same way: We measure some reflectance at the Earth’s surface. The smallest unit is called a ‘pixel’. In this paper, we do not question the pixel as an important and necessary entity. Instead, we argue for a somewhat different handling of our entities introducing the concepts of neighbourhood, distance and location. All these concepts are not new. In fact, entire disciplines like Geography are based on these conINTERFACING REMOTE SENSING AND GIS
663 citations
01 Aug 2001
TL;DR: An image-based modeling and editing system that takes a single photo as input and employs a suite of user-assisted techniques, based on a painting metaphor, to assign depths and extract layers, enabling editing from different viewpoints and modifying the shape, color, and illumination of these objects.
Abstract: We present an image-based modeling and editing system that takes a single photo as input. We represent a scene as a layered collection of depth images, where each pixel encodes both color and depth. Starting from an input image, we employ a suite of user-assisted techniques, based on a painting metaphor, to assign depths and extract layers. We introduce two specific editing operations. The first, a “clone brushing tool,” permits the distortion-free copying of parts of a picture, by using a parameterization optimization technique. The second, a “texture-illuminance decoupling filter,” discounts the effect of illumination on uniformly textured areas, by decoupling large- and small-scale features via bilateral filtering. Our system enables editing from different viewpoints, extracting and grouping of image-based objects, and modifying the shape, color, and illumination of these objects.
504 citations
References
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TL;DR: In this article, the authors studied square integrable coefficients of an irreducible representation of the non-unimodular $ax + b$-group and obtained explicit expressions in the case of a particular analyzing family that plays a role analogous to coherent states (Gabor wavelets) in the usual $L_2 $ -theory.
Abstract: An arbitrary square integrable real-valued function (or, equivalently, the associated Hardy function) can be conveniently analyzed into a suitable family of square integrable wavelets of constant shape, (i.e. obtained by shifts and dilations from any one of them.) The resulting integral transform is isometric and self-reciprocal if the wavelets satisfy an “admissibility condition” given here. Explicit expressions are obtained in the case of a particular analyzing family that plays a role analogous to that of coherent states (Gabor wavelets) in the usual $L_2 $ -theory. They are written in terms of a modified $\Gamma $-function that is introduced and studied. From the point of view of group theory, this paper is concerned with square integrable coefficients of an irreducible representation of the nonunimodular $ax + b$-group.
3,423 citations
Book•
04 Jan 1991TL;DR: This book describes an extended series of experiments into the role of geometry in the critical area of object recognition, providing precise definitions of the recognition and localization problems, the methods used to address them, the solutions to these problems, and the implications of this analysis.
Abstract: With contributions from Tomas LozanoPerez and Daniel P. Huttenlocher.An intelligent system must know "what "the objects are and "where "they are in its environment. Examples of this ubiquitous problem in computer vision arise in tasks involving hand-eye coordination (such as assembling or sorting), inspection tasks, gauging operations, and in navigation and localization of mobile robots. This book describes an extended series of experiments into the role of geometry in the critical area of object recognition. It provides precise definitions of the recognition and localization problems, describes the methods used to address them, analyzes the solutions to these problems, and addresses the implications of this analysis.The solution to problems of object recognition are of fundamental importance in many real applications and versions of the techniques described here are already being used in industrial settings. Although a number of questions remain to be solved, the authors provide a valuable framework for understanding both the strengths and limitations of using object shape to guide recognition.W. Eric L. Grimson is Matsushita Associate Professor in the Department of Electrical Engineering and Computer Science at MIT.Contents: Introduction. Recognition as a Search Problem. Searching for Correspondences. Two-Dimensional Constraints. Three-Dimensional Constraints. Verifying Hypotheses. Controlling the Search Explosion. Selecting Subspaces of the Search Space. Empirical Testing. The Combinatorics of the Matching Process. The Combinatorics of Hough Transforms. The Combinatorics of Verification. The Combinatorics of Indexing. Evaluating the Methods. Recognition from Libraries. Parameterized Objects. The Role of Grouping. Sensing Strategies. Applications. The Next Steps.
896 citations
01 Jan 1985
TL;DR: In this article, the adaptive least square correlation (ALES) is used for image matching, which allows for simultaneous radiometric corrections and local geometrical image shaping, whereby the system parameters are automatically assessed, corrected, and thus optimized during the least squares iterations.
Abstract: The Adaptive Least Squares Correlation is a very potent and flexible technique for all kinds of data matching problems. Here its application to image matching is outlined. It allows for simultaneous radiometric corrections and local geometrical image shaping, whereby the system parameters are automatically assessed, corrected, and thus optimized during the least squares iterations. The various tools of least squares estimation can be favourably utilized for the assessment of the correlation quality. Furthermore, the system allows for stabilization and improvement of the correlation procedure through the simultaneous consideration of geometrical constraints, e.g. the collinearity condition. Some exciting new perspectives are emphasized, as for example multiphoto correlation, multitemporal and multisensor correlation, multipoint correlation, and simultaneous correlation/triangulation.
667 citations
TL;DR: A new approach for tracking roads from satellite images, and thereby illustrate a general computational strategy for tracking 1D structures and other recognition tasks in computer vision, related to recent work in active vision and motivated by the "divide-and-conquer" strategy of parlour games.
Abstract: We present a new approach for tracking roads from satellite images, and thereby illustrate a general computational strategy ("active testing") for tracking 1D structures and other recognition tasks in computer vision. Our approach is related to recent work in active vision on "where to look next" and motivated by the "divide-and-conquer" strategy of parlour games. We choose "tests" (matched filters for short road segments) one at a time in order to remove as much uncertainty as possible about the "true hypothesis" (road position) given the results of the previous tests. The tests are chosen online based on a statistical model for the joint distribution of tests and hypotheses. The problem of minimizing uncertainty (measured by entropy) is formulated in simple and explicit analytical terms. At each iteration new image data are examined and a new entropy minimization problem is solved (exactly), resulting in a new image location to inspect, and so forth. We report experiments using panchromatic SPOT satellite imagery with a ground resolution of ten meters.
580 citations
TL;DR: In this article, the authors describe the organization of a rule-based system, SPAM, that uses map and domain-specific knowledge to interpret airport scenes, and the results of the system's analysis are characterized by the labeling of individual regions in the image and the collection of these regions into consistent interpretations of the major components of an airport model.
Abstract: In this paper, we describe the organization of a rule-based system, SPAM, that uses map and domain-specific knowledge to interpret airport scenes. This research investigates the use of a rule-based system for the control of image processing and interpretation of results with respect to a world model, as well as the representation of the world model within an image/map database. We present results on the interpretation of a high-resolution airport scene wvhere the image segmentation has been performed by a human, and by a region-based image segmentation program. The results of the system's analysis is characterized by the labeling of individual regions in the image and the collection of these regions into consistent interpretations of the major components of an airport model. These interpretations are ranked on the basis of their overall spatial and structural consistency. Some evaluations based on the results from three evolutionary versions of SPAM are presented.
420 citations